Neural networks letter: A novel joint-processing adaptive nonlinear equalizer using a modular recurrent neural network for chaotic communication systems

Authors:
Haiquan Zhao;Xiangping Zeng;Jiashu Zhang;Yangguang Liu;Xiaomin Wang;Tianrui Li
Affiliations:
School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 610031, China and School of Information Science & Technology, Southwest Jiaotong University, Chengdu, 610031, China;School of Information Science & Technology, Southwest Jiaotong University, Chengdu, 610031, China and Center of Electronic Lab, Chengdu University of Information Technology, Chengdu, 610255, China;School of Information Science & Technology, Southwest Jiaotong University, Chengdu, 610031, China;Ningbo Institute of Technology, Zhejiang University, Ningbo, 315100, China;School of Information Science & Technology, Southwest Jiaotong University, Chengdu, 610031, China;School of Information Science & Technology, Southwest Jiaotong University, Chengdu, 610031, China
Venue:
Neural Networks
Year:
2011

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Abstract

To eliminate nonlinear channel distortion in chaotic communication systems, a novel joint-processing adaptive nonlinear equalizer based on a pipelined recurrent neural network (JPRNN) is proposed, using a modified real-time recurrent learning (RTRL) algorithm. Furthermore, an adaptive amplitude RTRL algorithm is adopted to overcome the deteriorating effect introduced by the nesting process. Computer simulations illustrate that the proposed equalizer outperforms the pipelined recurrent neural network (PRNN) and recurrent neural network (RNN) equalizers.